Multiplexed biomarker analysis is more powerful in reflecting the biological behaviors of a tumor than single biomarker analysis, but its standardization and quantification is still a challenge. Furthermore, most computer software does not provide methods for imaging and analyzing subcellular localization of biomarkers and correlating them with biological and clinical information. The objective of this project is to develop a platform which combines imaging and quantification of multiplexed immunostaining plus bioinformatics for the prediction of lymph node metastases (LNM) from the primary tumor (PT) of squamous cell carcinoma of the head and neck (SCCHN). LNM of SCCHN is a precisely defined biological phenomenon which is an ideal model to be utilized to develop this multiplexed biomarker platform (MBP). Based on our preliminary studies, we aim to test the hypothesis that that the MBP can be developed to identify the subcellular distribution and expression of multiple metastasis-related biomarkers simultaneously in PTs. Accurate quantification of these biomarkers will facilitate the prediction of metastasis from PTs. Three emerging technologies, quantum dot (QD)-based immunohistofluorescence (IHF), multispectral imaging, and machine learning will be used to test this hypothesis. Using these approaches, a platform that combines quantifying multiplexed immunostaining with biostatistics will be developed and tested for its sensitivity, specificity, and prediction power for use in the clinic. Therefore, this project fits appropriately to the scope of the NCI program announcement Developmental Research in Cancer Prognosis and Prediction (PA-09-159). Three aims are proposed in the study. (1) To develop a multiplexed biomarker system and method based on a bulk tissue model for prediction of LNM in SCCHN PT tissues. This Aim will establish and validate an analysis methodology for multiplexed quantification of membrane and cytoplasmic staining using a new function in InForm software where subcellular localization of certain biomarkers will be specifically analyzed. Prediction of LNM based on this bulk tissue model will be achieved. (2) To develop a per-cell quantification method based on a sub-population model for prediction of LNM in SCCHN PT tissue. The per-cell analysis results will quantified as the percentage of high risk cells from the multiplexed biomarker analyses in the same PTs. The high risk population will be correlated with LNM. The sensitivity and specificity of the prediction by the sub-population model will be compared with that of the bulk tissue model. (3) To develop and validate a nomogram with software combining clinical characterizations of metastasis as a working platform for the prediction of LNM. While the primary endpoint of Aim 1 and 2 is to correlate the three biomarkers with metastasis, other clinical factors such as differentiation status, tumor stage, and site, etc. may also correlate with LNM. The most predictive biomarker set combined with relevant clinical factors will constitute a platform with computer software that will be validated in an additional 100 SCCHN samples for prediction of LNM.